{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 692,
     "status": "ok",
     "timestamp": 1649955038244,
     "user": {
      "displayName": "Sam Lu",
      "userId": "15789059763790170725"
     },
     "user_tz": -480
    },
    "id": "dB-atd8Y-vy7"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "import time\n",
    "\n",
    "class CliffWalkingEnv:\n",
    "    def __init__(self, ncol, nrow):\n",
    "        self.nrow = nrow  ## 网格行\n",
    "        self.ncol = ncol  ## 网格列\n",
    "        self.x = 0  # 记录当前智能体位置的横坐标\n",
    "        self.y = self.nrow - 1  # 记录当前智能体位置的纵坐标\n",
    "\n",
    "    ## step 前进一步的，拿到下一个状态，对应的奖励\n",
    "    def step(self, action):  # 外部调用这个函数来改变当前位置\n",
    "        # 4种动作, change[0]:上, change[1]:下, change[2]:左, change[3]:右。坐标系原点(0,0)\n",
    "        # 定义在左上角\n",
    "        change = [[0, -1], [0, 1], [-1, 0], [1, 0]] ## 动作\n",
    "        self.x = min(self.ncol - 1, max(0, self.x + change[action][0])) ## 下一个状态x到了网格外部，就保持不动的\n",
    "        self.y = min(self.nrow - 1, max(0, self.y + change[action][1])) ## 下一个状态y到了网格外部，就保持不动的\n",
    "        next_state = self.y * self.ncol + self.x    ## 下一个状态的坐标\n",
    "        reward = -1         ## 默认的奖励都是 -1\n",
    "        done = False        ## 是否终止了的\n",
    "        if self.y == self.nrow - 1 and self.x > 0:  # 下一个位置在悬崖或者目标\n",
    "            done = True  ## 终止\n",
    "            if self.x != self.ncol - 1: ## 下一个目标是悬崖，奖励= -100\n",
    "                reward = -100\n",
    "        return next_state, reward, done   ## 依次返回了  下一个状态、奖励、是否终止的\n",
    "\n",
    "    ## 重置到起始点\n",
    "    def reset(self):  # 回归初始状态,起点在左上角\n",
    "        self.x = 0\n",
    "        self.y = self.nrow - 1\n",
    "        return self.y * self.ncol + self.x     ## reset以后起始的坐标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1649955038245,
     "user": {
      "displayName": "Sam Lu",
      "userId": "15789059763790170725"
     },
     "user_tz": -480
    },
    "id": "VZx5sBiD-vy9"
   },
   "outputs": [],
   "source": [
    "class DynaQ:\n",
    "    \"\"\" Dyna-Q算法 \"\"\"\n",
    "    def __init__(self,\n",
    "                 ncol,\n",
    "                 nrow,\n",
    "                 epsilon,\n",
    "                 alpha,\n",
    "                 gamma,\n",
    "                 n_planning,\n",
    "                 n_action=4):\n",
    "        self.Q_table = np.zeros([nrow * ncol, n_action])  # 初始化 Q(s,a) 表格\n",
    "        self.n_action = n_action  # 动作个数\n",
    "        self.alpha = alpha  # 学习率\n",
    "        self.gamma = gamma  # 折扣因子\n",
    "        self.epsilon = epsilon  # epsilon-贪婪策略中的参数\n",
    "\n",
    "        self.n_planning = n_planning  #执行Q-planning的次数, 对应1次Q-learning\n",
    "        ## 保存历史交互的数据，也就是智能体和\n",
    "        self.model = dict()  # 环境模型\n",
    "\n",
    "    ## 获取当前采取的行动，采取了ε-贪婪策略\n",
    "    def take_action(self, state):  # 选取下一步的操作,具体实现为epsilon-贪婪\n",
    "        if np.random.random() < self.epsilon:           ##  随机选择一个动作\n",
    "            action = np.random.randint(self.n_action)   ##  拿到状态下对应的动作\n",
    "        else:\n",
    "            action = np.argmax(self.Q_table[state])     ##  拿到动作价值最大的动作\n",
    "        return action\n",
    "\n",
    "    '''\n",
    "    增量更新动作价值函数，对应相应的Q-learning公式\n",
    "    和sarsa algorithm不同的地方就是，这使用了状态内所有动作的最大值，但是sarsa直接使用了(s, a)状态动作对的值\n",
    "    sarsa的公式就是：\n",
    "    s0, a0分别是当前的状态和动作，r是当前动作的奖励；s1是下一个状态\n",
    "    def update(self, s0, a0, r, s1, a1):\n",
    "        td_error = r + self.gamma * self.Q_table[s1, a1] - self.Q_table[s0, a0]    ## 算时序差分的\n",
    "        self.Q_table[s0, a0] += self.alpha * td_error      ##  更新动作价值函数\n",
    "    self.Q_table  也就是当前的状态动作对应的动作价值\n",
    "    '''\n",
    "    def q_learning(self, s0, a0, r, s1):\n",
    "        td_error = r + self.gamma * self.Q_table[s1].max() - self.Q_table[s0, a0]\n",
    "        self.Q_table[s0, a0] += self.alpha * td_error\n",
    "\n",
    "    '''\n",
    "        增量更新动作价值函数\n",
    "      . 使用了环境模型给出的数据，也就是智能体和环境交互实时给出的数据\n",
    "      . 使用了保存起来的经验数据，也就是历史数据\n",
    "    '''\n",
    "    def update(self, s0, a0, r, s1):\n",
    "        ## 智能体和环境交互得到的一个数据，用来update模型的\n",
    "        self.q_learning(s0, a0, r, s1)     ## 使用 智能体和环境 交互得到的 s0,a0, r, s1 来 update 动作价值函数，也就是学习 Q_table 的。\n",
    "        ## 保存历史数据，也就是保存智能体和环境一次交互得到的经验数据，用来后续离线训练使用\n",
    "        self.model[(s0, a0)] = r, s1  # 将数据添加到模型中\n",
    "        ## 通过历史数据来训练 Q_table 动作价值函数，历史数据训练的次数是self.n_planning \n",
    "        for _ in range(self.n_planning):  # Q-planning循环\n",
    "            # 随机选择曾经遇到过的状态动作对\n",
    "            (s, a), (r, s_) = random.choice(list(self.model.items()))\n",
    "            self.q_learning(s, a, r, s_)   ## 使用历史数据来 update Q_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1649955038246,
     "user": {
      "displayName": "Sam Lu",
      "userId": "15789059763790170725"
     },
     "user_tz": -480
    },
    "id": "NOMaPWZS-vy9"
   },
   "outputs": [],
   "source": [
    "def DynaQ_CliffWalking(n_planning):\n",
    "    ncol = 12  ##  列\n",
    "    nrow = 4   ##  行\n",
    "    env = CliffWalkingEnv(ncol, nrow)  ##  实例化悬崖环境 env\n",
    "    epsilon = 0.01        ##  ε的值指定是 0.1\n",
    "    alpha = 0.1           ##  α的值初始化是 0.1\n",
    "    gamma = 0.9           ##  γ的值初始化是 0.9\n",
    "    agent = DynaQ(ncol, nrow, epsilon, alpha, gamma, n_planning)      ##  实例化DynaQ\n",
    "    num_episodes = 300  # 智能体在环境中运行多少条序列\n",
    "\n",
    "    return_list = []  # 记录每一条序列的回报\n",
    "    for i in range(10):  # 显示10个进度条\n",
    "        # tqdm的进度条功能\n",
    "        with tqdm(total=int(num_episodes / 10),\n",
    "                  desc='Iteration %d' % i) as pbar:\n",
    "            for i_episode in range(int(num_episodes / 10)):  # 每个进度条的序列数\n",
    "                episode_return = 0                 ##  序列返回值\n",
    "                state = env.reset()                ##  初始化环境，并给出初始状态 state\n",
    "                done = False                       ##  是否终止的\n",
    "                while not done:                    ##  没有终止就执行\n",
    "                    action = agent.take_action(state)           ## 智能体使用ε-贪婪策略选择动作 action \n",
    "                    next_state, reward, done = env.step(action)           ## 智能体前进一步，拿到下一个状态、奖励，是否终止的\n",
    "                    episode_return += reward  # 这里回报的计算不进行折扣因子衰减\n",
    "                    '''\n",
    "                    增量更新动作价值函数\n",
    "                    . 使用了环境模型给出的数据，也就是智能体和环境交互实时给出的数据，每次交互只有一个数据，所以每次都只有一个实时交互数据，用来update\n",
    "                    . 使用了保存起来的经验数据，也就是历史数据，每次随意挑出 n_planning 个保存起来的历史数据\n",
    "                    '''\n",
    "                    agent.update(state, action, reward, next_state)\n",
    "                    state = next_state ## 赋值给当前状态\n",
    "                return_list.append(episode_return)\n",
    "                if (i_episode + 1) % 10 == 0:  # 每10条序列打印一下这10条序列的平均回报\n",
    "                    pbar.set_postfix({\n",
    "                        'episode':\n",
    "                        '%d' % (num_episodes / 10 * i + i_episode + 1),\n",
    "                        'return':\n",
    "                        '%.3f' % np.mean(return_list[-10:])\n",
    "                    })\n",
    "                pbar.update(1)\n",
    "    return return_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 868
    },
    "executionInfo": {
     "elapsed": 4145,
     "status": "ok",
     "timestamp": 1649955042382,
     "user": {
      "displayName": "Sam Lu",
      "userId": "15789059763790170725"
     },
     "user_tz": -480
    },
    "id": "26Jk6HKF-vy-",
    "outputId": "5bfb06b4-aacd-4fe1-ab92-c0e216e56dc6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-planning步数为：0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 413.38it/s, episode=30, return=-138.400]\n",
      "Iteration 1: 100%|████████████████████████████████████████| 30/30 [00:00<00:00, 749.93it/s, episode=60, return=-64.100]\n",
      "Iteration 2: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 1153.82it/s, episode=90, return=-46.000]\n",
      "Iteration 3: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1250.00it/s, episode=120, return=-38.000]\n",
      "Iteration 4: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1153.61it/s, episode=150, return=-28.600]\n",
      "Iteration 5: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1364.50it/s, episode=180, return=-25.300]\n",
      "Iteration 6: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1666.59it/s, episode=210, return=-23.600]\n",
      "Iteration 7: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 2140.64it/s, episode=240, return=-20.100]\n",
      "Iteration 8: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 2500.13it/s, episode=270, return=-17.100]\n",
      "Iteration 9: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1764.22it/s, episode=300, return=-16.500]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-planning步数为：2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|████████████████████████████████████████| 30/30 [00:00<00:00, 422.54it/s, episode=30, return=-53.800]\n",
      "Iteration 1: 100%|████████████████████████████████████████| 30/30 [00:00<00:00, 666.68it/s, episode=60, return=-37.100]\n",
      "Iteration 2: 100%|████████████████████████████████████████| 30/30 [00:00<00:00, 857.13it/s, episode=90, return=-23.600]\n",
      "Iteration 3: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1071.42it/s, episode=120, return=-18.500]\n",
      "Iteration 4: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1153.94it/s, episode=150, return=-16.400]\n",
      "Iteration 5: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1034.49it/s, episode=180, return=-16.400]\n",
      "Iteration 6: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 909.09it/s, episode=210, return=-13.400]\n",
      "Iteration 7: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1363.66it/s, episode=240, return=-13.200]\n",
      "Iteration 8: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1666.63it/s, episode=270, return=-13.200]\n",
      "Iteration 9: 100%|██████████████████████████████████████| 30/30 [00:00<00:00, 1764.73it/s, episode=300, return=-13.500]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-planning步数为：20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|████████████████████████████████████████| 30/30 [00:00<00:00, 192.13it/s, episode=30, return=-18.500]\n",
      "Iteration 1: 100%|████████████████████████████████████████| 30/30 [00:00<00:00, 394.74it/s, episode=60, return=-13.600]\n",
      "Iteration 2: 100%|████████████████████████████████████████| 30/30 [00:00<00:00, 412.82it/s, episode=90, return=-13.000]\n",
      "Iteration 3: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 389.61it/s, episode=120, return=-13.500]\n",
      "Iteration 4: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 310.91it/s, episode=150, return=-13.500]\n",
      "Iteration 5: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 374.88it/s, episode=180, return=-13.000]\n",
      "Iteration 6: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 400.08it/s, episode=210, return=-22.000]\n",
      "Iteration 7: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 340.91it/s, episode=240, return=-23.200]\n",
      "Iteration 8: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 389.64it/s, episode=270, return=-13.000]\n",
      "Iteration 9: 100%|███████████████████████████████████████| 30/30 [00:00<00:00, 405.40it/s, episode=300, return=-13.400]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "random.seed(0)\n",
    "n_planning_list = [0, 2, 20]                         ## 使用多少历史经验数据来 update 模型\n",
    "for n_planning in n_planning_list:\n",
    "    print('Q-planning步数为：%d' % n_planning)\n",
    "    time.sleep(0.5)\n",
    "    return_list = DynaQ_CliffWalking(n_planning)     ## 实例化使用了 DynaQ 方式的悬崖行走\n",
    "    episodes_list = list(range(len(return_list)))    ## 返回的序列的回报或者奖励\n",
    "    plt.plot(episodes_list,\n",
    "             return_list,\n",
    "             label=str(n_planning) + ' planning steps')\n",
    "plt.legend()\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('Dyna-Q on {}'.format('Cliff Walking'))\n",
    "plt.show()\n",
    "\n",
    "# Q-planning步数为：0\n",
    "\n",
    "# Iteration 0: 100%|██████████| 30/30 [00:00<00:00, 615.42it/s, episode=30,\n",
    "# return=-138.400]\n",
    "# Iteration 1: 100%|██████████| 30/30 [00:00<00:00, 1079.50it/s, episode=60,\n",
    "# return=-64.100]\n",
    "# Iteration 2: 100%|██████████| 30/30 [00:00<00:00, 1303.35it/s, episode=90,\n",
    "# return=-46.000]\n",
    "# Iteration 3: 100%|██████████| 30/30 [00:00<00:00, 1169.51it/s, episode=120,\n",
    "# return=-38.000]\n",
    "# Iteration 4: 100%|██████████| 30/30 [00:00<00:00, 1806.96it/s, episode=150,\n",
    "# return=-28.600]\n",
    "# Iteration 5: 100%|██████████| 30/30 [00:00<00:00, 2303.21it/s, episode=180,\n",
    "# return=-25.300]\n",
    "# Iteration 6: 100%|██████████| 30/30 [00:00<00:00, 2473.64it/s, episode=210,\n",
    "# return=-23.600]\n",
    "# Iteration 7: 100%|██████████| 30/30 [00:00<00:00, 2344.37it/s, episode=240,\n",
    "# return=-20.100]\n",
    "# Iteration 8: 100%|██████████| 30/30 [00:00<00:00, 1735.84it/s, episode=270,\n",
    "# return=-17.100]\n",
    "# Iteration 9: 100%|██████████| 30/30 [00:00<00:00, 2827.94it/s, episode=300,\n",
    "# return=-16.500]\n",
    "\n",
    "# Q-planning步数为：2\n",
    "\n",
    "# Iteration 0: 100%|██████████| 30/30 [00:00<00:00, 425.09it/s, episode=30,\n",
    "# return=-53.800]\n",
    "# Iteration 1: 100%|██████████| 30/30 [00:00<00:00, 655.71it/s, episode=60,\n",
    "# return=-37.100]\n",
    "# Iteration 2: 100%|██████████| 30/30 [00:00<00:00, 799.69it/s, episode=90,\n",
    "# return=-23.600]\n",
    "# Iteration 3: 100%|██████████| 30/30 [00:00<00:00, 915.34it/s, episode=120,\n",
    "# return=-18.500]\n",
    "# Iteration 4: 100%|██████████| 30/30 [00:00<00:00, 1120.39it/s, episode=150,\n",
    "# return=-16.400]\n",
    "# Iteration 5: 100%|██████████| 30/30 [00:00<00:00, 1437.24it/s, episode=180,\n",
    "# return=-16.400]\n",
    "# Iteration 6: 100%|██████████| 30/30 [00:00<00:00, 1366.79it/s, episode=210,\n",
    "# return=-13.400]\n",
    "# Iteration 7: 100%|██████████| 30/30 [00:00<00:00, 1457.62it/s, episode=240,\n",
    "# return=-13.200]\n",
    "# Iteration 8: 100%|██████████| 30/30 [00:00<00:00, 1743.68it/s, episode=270,\n",
    "# return=-13.200]\n",
    "# Iteration 9: 100%|██████████| 30/30 [00:00<00:00, 1699.59it/s, episode=300,\n",
    "# return=-13.500]\n",
    "\n",
    "# Q-planning步数为：20\n",
    "\n",
    "# Iteration 0: 100%|██████████| 30/30 [00:00<00:00, 143.91it/s, episode=30,\n",
    "# return=-18.500]\n",
    "# Iteration 1: 100%|██████████| 30/30 [00:00<00:00, 268.53it/s, episode=60,\n",
    "# return=-13.600]\n",
    "# Iteration 2: 100%|██████████| 30/30 [00:00<00:00, 274.53it/s, episode=90,\n",
    "# return=-13.000]\n",
    "# Iteration 3: 100%|██████████| 30/30 [00:00<00:00, 264.25it/s, episode=120,\n",
    "# return=-13.500]\n",
    "# Iteration 4: 100%|██████████| 30/30 [00:00<00:00, 263.58it/s, episode=150,\n",
    "# return=-13.500]\n",
    "# Iteration 5: 100%|██████████| 30/30 [00:00<00:00, 245.27it/s, episode=180,\n",
    "# return=-13.000]\n",
    "# Iteration 6: 100%|██████████| 30/30 [00:00<00:00, 257.16it/s, episode=210,\n",
    "# return=-22.000]\n",
    "# Iteration 7: 100%|██████████| 30/30 [00:00<00:00, 257.08it/s, episode=240,\n",
    "# return=-23.200]\n",
    "# Iteration 8: 100%|██████████| 30/30 [00:00<00:00, 261.12it/s, episode=270,\n",
    "# return=-13.000]\n",
    "# Iteration 9: 100%|██████████| 30/30 [00:00<00:00, 213.01it/s, episode=300,\n",
    "# return=-13.400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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